FIG. 1 shows a high level diagram of a processing core 100 implemented with logic circuitry on a semiconductor chip. The processing core includes a pipeline 101. The pipeline consists of multiple stages each designed to perform a specific step in the multi-step process needed to fully execute a program code instruction. These typically include at least: 1) instruction fetch and decode; 2) data fetch; 3) execution; 4) write-back. The execution stage performs a specific operation identified by an instruction that was fetched and decoded in prior stage(s) (e.g., in step 1) above) upon data identified by the same instruction and fetched in another prior stage (e.g., step 2) above). The data that is operated upon is typically fetched from (general purpose) register storage space 102. New data that is created at the completion of the operation is also typically “written back” to register storage space (e.g., at stage 4) above).
The logic circuitry associated with the execution stage is typically composed of multiple “execution units” or “functional units” 103_1 to 103_N that are each designed to perform its own unique subset of operations (e.g., a first functional unit performs integer math operations, a second functional unit performs floating point instructions, a third functional unit performs load/store operations from/to cache/memory, etc.). The collection of all operations performed by all the functional units corresponds to the “instruction set” supported by the processing core 100.
Two types of processor architectures are widely recognized in the field of computer science: “scalar” and “vector”. A scalar processor is designed to execute instructions that perform operations on a single set of data, whereas, a vector processor is designed to execute instructions that perform operations on multiple sets of data. FIGS. 2A and 2B present a comparative example that demonstrates the basic difference between a scalar processor and a vector processor.
FIG. 2A shows an example of a scalar AND instruction in which a single operand set, A and B, are ANDed together to produce a singular (or “scalar”) result C (i.e., AB=C). By contrast, FIG. 2B shows an example of a vector AND instruction in which two operand sets, A/B and D/E, are respectively ANDed to produce a vector result C, F (i.e., A.AND.B=C and D.AND.E=F). As a matter of terminology, a “vector” is a data element having multiple “elements”. For example, a vector V=Q, R, S, T, U has five different elements: Q, R, S, T and U. The “size” of the exemplary vector V is five (because it has five elements).
FIG. 1 also shows the presence of vector register space 107 that is different than general purpose register space 102. Specifically, general purpose register space 102 is nominally used to store scalar values. As such, when, any of execution units perform scalar operations they nominally use operands called from (and write results back to) general purpose register storage space 102. By contrast, when any of the execution units perform vector operations they nominally use operands called from (and write results back to) vector register space 107. Different regions of memory may likewise be allocated for the storage of scalar values and vector values.
Note also the presence of masking logic 104_1 to 104_N and 105_1 to 105_N at the respective inputs to and outputs from the functional units 103_1 to 103_N. In various implementations, for vector operations, only one of these layers is actually implemented—although that is not a strict requirement (although not depicted in FIG. 1, conceivably, execution units that only perform scalar and not vector operations need not have any masking layer). For any vector instruction that employs masking, input masking logic 104_1 to 104_N and/or output masking logic 105_1 to 105_N may be used to control which elements are effectively operated on for the vector instruction. Here, a mask vector is read from a mask register space 106 (e.g., along with input operand vectors read from vector register storage space 107) and is presented to at least one of the masking logic 104, 105 layers.
Over the course of executing vector program code each vector instruction need not require a full data word. For example, the input vectors for some instructions may only be 8 elements, the input vectors for other instructions may be 16 elements, the input vectors for other instructions may be 32 elements, etc. Masking layers 104/105 are therefore used to identify a set of elements of a full vector data word that apply for a particular instruction so as to effect different vector sizes across instructions. Typically, for each vector instruction, a specific mask pattern kept in mask register space 106 is called out by the instruction, fetched from mask register space and provided to either or both of the mask layers 104/105 to “enable” the correct set of elements for the particular vector operation.
FIG. 3a shows an “integral image” calculation. An integral image calculation sums all pixel values over an image surface area 301 (e.g., as defined by the rectangular area having corner coordinates (0,0) and (x′,y′). The summing of the pixel values over the image surface area 301 essentially corresponds to the calculation of a discrete integral where the pixel values correspond to the discrete function being integrated over the surface area.
One technique is to sum all values along a first dimension of the area 301 for each discrete location along the second dimension. For example, as observed in FIG. 3a, all pixel values along the x axis within the surface area 301 are summed 302 for a particular location on the y axis within the surface area 301. Performing this operation for each y axis location within the surface area 301 yields a summation value for each y axis location. These summation values are then summed (essentially over the second (y) axis) to produce a final summation value for the entire surface area 301.
A problem is the efficiency at which an integral image is determined—particularly when multiple surface areas with overlapping pixel values are calculated. Presently scalar as opposed to vector sequences are utilized. FIG. 3b shows an example of a scalar instruction execution sequence used to calculate summations for different surface area widths. For example, resultant 311 corresponds to a surface area width of two, while resultant 312 corresponds to a surface area width of four. Here, the strictly serial and scalar nature of the ADD operations results in three machine cycles being needed to calculate summations for only four different widths. Said another way, as observed in FIG. 3b, a next advancement in width summation needs to wait for previous addition(s) over the smaller width(s).
In order to deal with the inefficiencies associated with the calculation of an image integral, alternative approaches have used program code sequences that employ algorithms that “approximate” the actual calculation. Approximations can be insufficient when accuracy is desired.